Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials

• URL: http://arxiv.org/abs/2210.10783v1
• Date: Wed, 19 Oct 2022 12:20:07 GMT
• Title: Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials
• Authors: Ismael Ben-Yelun, Miguel Diaz-Lago, Luis Saucedo-Mora, Miguel Angel Sanz, Ricardo Callado, Francisco Javier Montans
• Abstract summary: We develop an ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage. The predictions achieve a good level of accuracy, similar to other ML algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem. In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren-Miner index) in composite materials by processing the signals of Lamb Waves -- a non-destructive SHM technique -- with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude. The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an \textit{online learning} context where streams of data are fed continuously to the model, avoids repeated training -- essential for reliable real-time, continuous monitoring.

Related papers

• Scaling Laws for Predicting Downstream Performance in LLMs [75.28559015477137]
  This work focuses on the pre-training loss as a more-efficient metric for performance estimation. We extend the power law analytical function to predict domain-specific pre-training loss based on FLOPs across data sources. We employ a two-layer neural network to model the non-linear relationship between multiple domain-specific loss and downstream performance.
  arXiv  Detail & Related papers  (2024-10-11T04:57:48Z)
• Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes [0.0]
  We introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity. We identify optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.
  arXiv  Detail & Related papers  (2024-08-31T15:41:10Z)
• Higher order quantum reservoir computing for non-intrusive reduced-order models [0.0]
  Quantum reservoir computing technique (QRC) is a hybrid quantum-classical framework employing an ensemble of interconnected small quantum systems. We show that QRC is able to predict complex nonlinear dynamical systems in a stable and accurate manner.
  arXiv  Detail & Related papers  (2024-07-31T13:37:04Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Large-Scale OD Matrix Estimation with A Deep Learning Method [70.78575952309023]
  The proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset.
  arXiv  Detail & Related papers  (2023-10-09T14:30:06Z)
• Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimization [0.0]
  Guided wave propagation (GWP) is commonly employed for the inspection of structures in structural health monitoring (SHM) Uncertainty quantification (UQ) is regularly applied to improve the reliability of predictions. We propose a machine learning (ML)-based reduced order model (ROM) to decrease the computational time related to the simulation of the GWP.
  arXiv  Detail & Related papers  (2023-07-18T22:03:43Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• A Data-Driven Machine Learning Approach for Consumer Modeling with Load Disaggregation [1.6058099298620423]
  We propose a generic class of data-driven semiparametric models derived from consumption data of residential consumers. In the first stage, disaggregation of the load into fixed and shiftable components is accomplished by means of a hybrid algorithm. In the second stage, the model parameters are estimated using an L2-norm, epsilon-insensitive regression approach.
  arXiv  Detail & Related papers  (2020-11-04T13:36:11Z)
• Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework [1.3858051019755282]
  Failure in brittle materials led by micro- to macro-cracks under repetitive or increasing loads is often catastrophic. We develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels.
  arXiv  Detail & Related papers  (2020-03-24T17:13:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.